A large dust capacity air purification data analysis processing method and system

Abstract

The application provides a large dust capacity air purification data analysis processing method and system, acquires real-time dust particle concentration data and converts the same into a dust load monitoring signal; constructs a turbulence suppression configuration and configures the same in an air flow channel to form a low-resistance air flow channel structure; analyzes a change trend of the dust load monitoring signal, generates a dynamic control parameter set, and inputs the same into a purification control unit in the low-resistance air flow channel structure to control and adjust a fan rotating speed and a filtering electric field intensity in the low-resistance air flow channel structure; when the dust load monitoring signal exceeds a preset fluctuation threshold value, the turbulence suppression configuration, the controlled and adjusted fan rotating speed, and the controlled and adjusted filtering electric field intensity are combined to generate a purification efficiency stabilization scheme. The application breaks through a composite technical bottleneck of a sharp increase in turbulence resistance and a control response lag in a high dust concentration environment, and realizes real-time stabilization of purification efficiency under a large dust capacity state.

Description

Technical Field

[0001] This invention relates to the field of data analysis and processing technology, and in particular to a method and system for analyzing and processing data from large-capacity air purification systems. Background Technology

[0002] With the increasing prevalence of high-dust-concentration environments in industrial processing sites, traditional fixed-parameter purification methods can no longer meet the demands for efficient and stable purification. Air purification equipment faces more complex and dynamic operating conditions. Therefore, there is an urgent need for a data analysis and processing method that can sense changes in dust load in real time and dynamically adjust the operating parameters of air purification devices accordingly, in order to improve purification efficiency and extend equipment lifespan.

[0003] The current mainstream solution is an air purification control system based on multi-sensor fusion and fixed feedback rules. This system collects particulate matter concentration data in the environment in real time by deploying multiple air quality sensors to perform graded control on fan speed and filter module operation. For example, when the dust concentration is detected to increase, the system automatically switches to high-power mode to achieve a certain degree of adaptive purification operation.

[0004] Research has found that existing solutions have some inherent defects, including relying on fixed feedback rules, which makes it difficult to adjust according to the changing trend of dust concentration, and easily leads to problems of excessive or insufficient purification capacity; the lack of optimization design of the internal airflow structure of the air purification device results in increased wind resistance and decreased purification efficiency under high load conditions, affecting the overall operational stability and energy efficiency performance, and limiting its applicability in scenarios with dynamic changes in high dust concentration. Summary of the Invention

[0005] This invention provides a method and system for analyzing and processing large-capacity air purification data, which solves the problems in the prior art that rely on fixed feedback rules, making it difficult to adjust according to the changing trend of dust concentration, and easily causing excessive or insufficient purification capacity; and the lack of optimized design of the internal airflow structure of the air purification device, resulting in increased wind resistance and decreased purification efficiency under high load conditions, affecting the overall operational stability and energy efficiency, and limiting its applicability in scenarios with dynamic changes in high dust concentration.

[0006] In a first aspect, the present invention provides a method for analyzing and processing large-capacity dust-laden air purification data, comprising:

[0007] Collect real-time dust particle concentration data in a preset air purification area, and convert the real-time dust particle concentration data into a dust load monitoring signal;

[0008] A turbulence suppression configuration is constructed and placed within the airflow channel of a pre-defined air purification device to form a low-resistance airflow channel structure.

[0009] The changing trend of the dust load monitoring signal is analyzed based on a preset adaptive feedback algorithm to generate a dynamic control parameter set.

[0010] The set of dynamic control parameters is input into the purification control unit in the low-resistance airflow channel structure. Based on the purification control unit, the fan speed and filter electric field intensity in the low-resistance airflow channel structure are controlled and adjusted to generate the controlled and adjusted fan speed and filter electric field intensity.

[0011] When the dust load monitoring signal exceeds the preset fluctuation threshold, the turbulence suppression configuration, the controlled and adjusted fan speed, and the controlled and adjusted filter electric field strength are combined to generate a purification efficiency stabilization scheme for large dust volume air purification data analysis and processing.

[0012] Optionally, real-time dust particle concentration data within a preset air purification area is collected, and the real-time dust particle concentration data is converted into a dust load monitoring signal, including:

[0013] The real-time dust particle concentration data in the preset air purification area is obtained by a particulate matter sensor, and the real-time dust particle concentration data is quantified to generate an original dust concentration electrical signal.

[0014] The original dust concentration electrical signal is divided into multiple consecutive time window electrical signal segments, and the peak-to-valley difference of the signal amplitude in each electrical signal segment is calculated to generate a set of local fluctuation feature values.

[0015] The largest local fluctuation feature value is selected from the set of local fluctuation feature values, and the largest local fluctuation feature value is multiplied by a preset dust density coefficient to generate a dust load index.

[0016] The dust load index is linearly scaled to generate a dust load monitoring signal.

[0017] Optionally, a turbulence suppression configuration is constructed, and the turbulence suppression configuration is configured within the airflow channel of a preset air purification device to form a low-resistance airflow channel structure, including:

[0018] Based on the preset large dust-holding airflow characteristic parameters, a guide surface configuration is constructed, and the discrete point spatial coordinates of the geometric surface of the guide surface configuration are measured to generate an initial surface coordinate set.

[0019] Curvature constraints are applied to each initial surface coordinate point in the initial surface coordinate set to adjust the position of the initial surface coordinate points, thereby generating an optimized surface coordinate set.

[0020] The optimized surface coordinate set is divided according to a preset unit spacing threshold to generate multiple independent flow guiding units;

[0021] The reference spacing between adjacent independent flow guiding units is measured based on a preset stacking direction. The installation anchor point of each independent flow guiding unit is located according to the reference spacing. Each installation anchor point is connected to form a flow guiding stacking structure.

[0022] The airflow guiding layer structure is positioned within the airflow channel of a pre-set air purification device to form a low-resistance airflow channel structure.

[0023] Optionally, the changing trend of the dust load monitoring signal is analyzed based on a preset adaptive feedback algorithm to generate a dynamic control parameter set, including:

[0024] The dust load monitoring signal is received, and the dust load monitoring signal is divided into multiple consecutive time period signals based on a preset fixed duration.

[0025] Extract the maximum and minimum signal values ​​from each time period signal sequence, and calculate the difference between the maximum and minimum signal values ​​to generate the fluctuation amplitude value of the time period signal sequence;

[0026] Calculate the difference between two adjacent fluctuation amplitude values, obtain the absolute value of the difference, and use the absolute value as the fluctuation change value over a time period.

[0027] The fluctuation values ​​of multiple consecutive time periods are input into a preset relational mapping model to generate the fan speed parameter value and filter electric field strength parameter value corresponding to the pre-stored relational mapping model;

[0028] The fan speed parameter value and the filter electric field strength parameter value are combined to generate a dynamic control parameter set.

[0029] Optionally, the dynamic control parameter set is input into the purification control unit in the low-resistance airflow channel structure, and the fan speed and filter electric field strength in the low-resistance airflow channel structure are controlled and adjusted based on the purification control unit to generate the adjusted fan speed and the adjusted filter electric field strength, including:

[0030] Extract fan speed parameter values ​​and filter electric field strength parameter values ​​from the dynamic control parameter set;

[0031] The fan speed parameter value is matched to a preset fan control command range to generate a fan speed control command value, and the filter electric field strength parameter value is matched to a preset electric field control command range to generate a filter electric field strength control command value.

[0032] The fan drive module included in the purification control unit controls and adjusts the preset input voltage value of the fan motor corresponding to the fan speed control command value, and generates the controlled and adjusted input voltage value.

[0033] The discharge power of the preset high-voltage electrode corresponding to the filter electric field strength control command value is controlled and adjusted by the electric field generation module included in the purification control unit, thereby generating the controlled and adjusted discharge power value.

[0034] Read the actual speed value of the preset fan motor under the control and adjustment of the input voltage value, and use the actual speed value as the fan speed after control and adjustment;

[0035] The actual filtering electric field strength value of the preset high-voltage electrode under the action of the controlled and adjusted discharge power value is read, and the actual filtering electric field strength value is used as the controlled and adjusted filtering electric field strength.

[0036] Optionally, when the dust load monitoring signal exceeds a preset fluctuation threshold, the turbulence suppression configuration, the controlled and adjusted fan speed, and the controlled and adjusted filter electric field strength are combined to generate a stable purification efficiency scheme for large-capacity dust air purification data analysis and processing, including:

[0037] The dust load monitoring signal is detected to see if it exceeds a preset fluctuation threshold. When it exceeds the preset fluctuation threshold, the comprehensive evaluation value of the guide surface characteristic parameters of the turbulence suppression configuration is calculated to generate a turbulence suppression intensity value.

[0038] The ratio of the controlled and adjusted fan speed to the preset reference speed is calculated as the fan speed influence factor, and the ratio of the controlled and adjusted filter electric field strength to the preset reference electric field strength is calculated as the electric field strength influence factor.

[0039] The turbulence suppression intensity value, the fan speed influence factor, and the electric field intensity influence factor are weighted and fused to generate a collaborative control parameter set;

[0040] The collaborative control parameter group is matched to a preset purification rule base to generate a set of equipment control parameter adjustment instructions;

[0041] The device control parameter adjustment instruction set is executed to generate a stable purification efficiency scheme for large-capacity dust air purification data analysis and processing.

[0042] Optionally, the turbulence suppression intensity value, the fan speed influence factor, and the electric field intensity influence factor are weighted and fused to generate a collaborative control parameter set, including:

[0043] The preset first weighting coefficient, the preset second weighting coefficient, and the preset third weighting coefficient are respectively assigned to the turbulence suppression intensity value, the fan speed influence factor, and the electric field intensity influence factor to generate turbulence suppression weight allocation value, fan speed weight allocation value, and electric field intensity weight allocation value;

[0044] The turbulence suppression intensity value is multiplied by the turbulence suppression weight allocation value to generate a turbulence suppression weighting factor.

[0045] The fan speed influence factor is multiplied by the fan speed weight allocation value to generate the fan speed weighting factor.

[0046] The electric field strength influence factor is multiplied by the electric field strength weight allocation value to generate the electric field strength weighting factor.

[0047] The turbulence suppression weighting factor, the fan speed weighting factor, and the electric field strength weighting factor are vector normalized to generate a standardized cooperative control vector.

[0048] The standardized cooperative control vector is directly used as the cooperative control parameter set.

[0049] Secondly, the present invention provides a large-capacity dust-absorbing air purification data analysis and processing system, comprising:

[0050] The conversion module is used to collect real-time dust particle concentration data in a preset air purification area and convert the real-time dust particle concentration data into a dust load monitoring signal.

[0051] A configuration module is used to construct a turbulence suppression configuration, which is then configured within the airflow channel of a preset air purification device to form a low-resistance airflow channel structure.

[0052] The analysis module is used to analyze the changing trend of the dust load monitoring signal based on a preset adaptive feedback algorithm and generate a dynamic control parameter set.

[0053] The adjustment module is used to input the dynamic control parameter set into the purification control unit in the low-resistance airflow channel structure, and control and adjust the fan speed and filter electric field intensity in the low-resistance airflow channel structure based on the purification control unit, and generate the controlled and adjusted fan speed and filter electric field intensity.

[0054] The combined module is used to combine the turbulence suppression configuration, the controlled and adjusted fan speed, and the controlled and adjusted filter electric field strength when the dust load monitoring signal exceeds the preset fluctuation threshold, to generate a stable purification efficiency scheme for large dust volume air purification data analysis and processing.

[0055] Thirdly, the present invention provides a computing device including a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform a large-capacity air purification data analysis and processing method as described in any of the first aspects.

[0056] Fourthly, the present invention provides a computer storage medium storing computer program instructions thereon, wherein the computer program instructions, when executed by a processor, implement the large-capacity air purification data analysis and processing method described in any one of the first aspects.

[0057] This invention dynamically configures a turbulence suppression configuration in the airflow channel to form a low-resistance airflow channel structure. It combines an adaptive feedback algorithm to analyze dust load monitoring signals to generate a dynamic control parameter set, and synchronously adjusts the fan speed and filter electric field intensity based on the purification control unit. When the dust load monitoring signal exceeds a preset fluctuation threshold, the turbulence suppression configuration, the adjusted fan speed, and the filter electric field intensity are used together to overcome the combined technical bottleneck of increased turbulence resistance and delayed control response in high dust concentration environments, and achieve real-time stability of purification efficiency under large dust capacity conditions.

[0058] Furthermore, by quantizing real-time dust particle concentration data into raw electrical signals, segmenting them into electrical signal segments with continuous time windows, and calculating the peak-to-valley difference of the signal amplitude of each segment to generate a set of local fluctuation feature values, the maximum feature value is selected and fused with the dust density coefficient to generate a dust load index, which is then linearly scaled to output a dust load monitoring signal. In response to the instantaneous fluctuation characteristics of high dust concentration abrupt changes, a dedicated conversion link from raw data to control signals is constructed, providing a precise load sensing basis for the synergistic effect of turbulence suppression and dynamic control. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 A flowchart of a large-capacity dust-collecting air purification data analysis and processing method provided in an embodiment of the present invention;

[0061] Figure 2 This is a schematic diagram of the structure of a large-capacity air purification data analysis and processing system provided in an embodiment of the present invention;

[0062] Figure 3This is a schematic diagram of the structure of a computing device provided in an embodiment of the present invention. Detailed Implementation

[0063] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0064] In some of the processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0065] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0066] Figure 1 A flowchart of a large-capacity dust-collecting air purification data analysis and processing method is provided as an embodiment of the present invention, such as... Figure 1 As shown, the method includes:

[0067] In environments with high dust concentrations, existing air purification technologies face two major drawbacks: First, when dust concentrations fluctuate drastically, traditional filters experience a surge in airflow resistance due to increased turbulence, leading to rapid saturation of dust-holding capacity and a catastrophic drop in purification efficiency. Second, control systems based on fixed parameters cannot respond to sudden dust changes in real time, and the resulting lag in adjustment triggers a vicious cycle of soaring energy consumption and purification failure.

[0068] To address these issues, the design concept of the large dust-holding capacity air purification data analysis and processing method provided in this invention is as follows: A low-resistance airflow channel structure is formed by physically embedding a turbulence suppression configuration into the airflow channel, suppressing turbulence intensity at the source to maintain large dust-holding capacity; simultaneously, an adaptive feedback algorithm is used to analyze the abrupt change trend of the dust load monitoring signal, generating a dynamic control parameter set to drive the purification control unit to adjust the fan speed and filter electric field intensity in real time; finally, at the critical moment when the dust concentration exceeds the fluctuation threshold, the turbulence suppression configuration, the real-time adjusted fan speed, and the filter electric field intensity are linked to form a cross-domain synergistic mechanism, overcoming the dual technical barriers of physical limitations and control lag, and achieving rapid response purification operation in high dust abrupt change environments. Based on this, this invention provides a large dust-holding capacity air purification data analysis and processing method, such as... Figure 1 ,include:

[0069] Step 101: Collect real-time dust particle concentration data in the preset air purification area, and convert the real-time dust particle concentration data into a dust load monitoring signal.

[0070] In this step, real-time dust particle concentration data refers to physical quantity data that reflects the number of PM2.5 / PM10 particles per unit volume of air, continuously collected by a laser scattering particle sensor; the conversion operation refers to the physical process of standardizing the original current signal through an operational amplifier and converting it into a 0-10V voltage signal; the dust load monitoring signal refers to the dynamic voltage waveform generated after the conversion operation that is linearly related to the dust deposition rate.

[0071] In this embodiment of the invention, dust particle concentration data is first collected in real time by particulate matter sensors arranged within the air purification area. Then, the collected dust particle concentration data is input to a signal converter for linear amplification and dimensional conversion. Finally, a continuously changing dust load monitoring signal is generated. This dust load monitoring signal directly reflects the cumulative dust intensity within the air purification area per unit time.

[0072] Step 102: Construct a turbulence suppression configuration and place the turbulence suppression configuration in the airflow channel of a preset air purification device to form a low-resistance airflow channel structure.

[0073] In this step, the turbulence suppression configuration refers to the physical mechanism composed of guide surfaces and stacked units used to decompose airflow vortices; the configuration operation refers to the mechanical assembly process of installing the configuration into the airflow channel by aligning the positioning pins and tightening the bolts; the low-resistance airflow channel structure refers to the physical channel formed after configuring the turbulence suppression configuration, which reduces the airflow pressure loss by more than 15%.

[0074] In this embodiment of the invention, firstly, a guide surface with a specific curvature is constructed based on the principles of fluid dynamics as a turbulence suppression configuration. Secondly, this turbulence suppression configuration is assembled to the airflow channel wall inside the air purification device by bolt fastening. Finally, the airflow channel forms a flow channel structure with low eddy characteristics. This low-resistance airflow channel structure allows the airflow to pass through the purification area in a laminar state. Therefore, the large dust-holding air purification data analysis and processing method used in this embodiment of the invention also needs to be dynamically controlled according to the turbulence suppression configuration to adapt to changes in dust load. For specific operation steps, please refer to the subsequent technical content.

[0075] Step 103: Analyze the changing trend of the dust load monitoring signal based on the preset adaptive feedback algorithm to generate a dynamic control parameter set.

[0076] In this step, the preset adaptive feedback algorithm refers to the PID control model that dynamically adjusts control parameters based on historical data; the analysis operation refers to the mathematical processing of calculating the first derivative of the signal waveform and extracting the change characteristics; and the dynamic control parameter set refers to a binary array containing the speed regulation coefficient and the electric field strength correction value.

[0077] In this embodiment of the invention, the dust load monitoring signal is first input into a preset adaptive feedback algorithm module. Then, the algorithm calculates the slope of the signal change rate and identifies trend inflection points. Subsequently, a dynamic control parameter set, including a fan speed adjustment coefficient and an electric field strength correction value, is generated based on the trend duration and fluctuation amplitude. This parameter set is updated in real time to adapt to changes in dust load.

[0078] Step 104: Input the set of dynamic control parameters into the purification control unit in the low-resistance airflow channel structure, and control and adjust the fan speed and filter electric field intensity in the low-resistance airflow channel structure based on the purification control unit to generate the controlled and adjusted fan speed and filter electric field intensity.

[0079] In this step, the purification control unit refers to an embedded control system integrating a microprocessor and power drive circuit; the filter electric field strength refers to the kilovolt-level DC voltage applied between the two poles of the electrostatic dust collection plate; the control and adjustment operation refers to the physical control of changing the motor speed through pulse width modulation and adjusting the electric field strength through the voltage gain circuit; the fan speed after control and adjustment refers to the value of revolutions per minute maintained at the optimal airflow rate after adjustment; and the filter electric field strength after control and adjustment refers to the kilovolt voltage value that maximizes the particle charging efficiency after adjustment.

[0080] In this embodiment of the invention, the dynamic control parameter set is first transmitted to the decoding circuit of the purification control unit. Then, the microprocessor of this unit converts the speed adjustment coefficient into a pulse width modulation command to drive the fan motor. Simultaneously, an electric field correction value is applied to the high-voltage generator. Finally, the adjusted fan speed and the adjusted filter electric field strength are output. The adjusted speed is directly proportional to the electric field strength.

[0081] Step 105: When the dust load monitoring signal exceeds the preset fluctuation threshold, the turbulence suppression configuration, the controlled and adjusted fan speed, and the controlled and adjusted filter electric field strength are combined to generate a purification efficiency stabilization scheme for large dust volume air purification data analysis and processing.

[0082] In this step, the combined operation refers to the physical coordination of the three parameters to be executed synchronously through mechanical transmission mechanism, motor control circuit and high voltage generator; the purification efficiency stabilization scheme refers to the parameter combination strategy that controls the fluctuation range of CADR value within five percent.

[0083] In this embodiment of the invention, when the dust load monitoring signal exceeds the fluctuation threshold, the flow-guiding surface deflection mechanism of the turbulence suppression configuration is first activated. Secondly, the controlled and adjusted fan speed and the controlled and adjusted filter electric field intensity are synchronized in phase via a co-controller. Finally, the physical combination of these three elements spatially matches the airflow distribution, particle adsorption force, and electric field coverage area, generating a stable scheme that maintains constant purification efficiency. The fluctuation threshold is a fixed value set based on experiments and experience.

[0084] For example, firstly, a particulate sensor installed in the factory workshop collects data on the concentration of 5,000 micrograms of dust particles per cubic meter in the air. This data is then converted into a 4-volt dust load monitoring signal. Next, a guide surface configuration is constructed based on preset airfoil parameters and assembled into the air duct of the purification equipment using positioning bolts to form a low-resistance flow channel. Subsequently, an adaptive feedback algorithm is used to analyze the signal's upward trend, generating a dynamic control parameter set that includes a 0.8-point speed coefficient and a 12-kilovolt electric field correction value. This parameter set is then input into the purification control unit, which outputs a fan speed of 1,200 revolutions per minute and a 12-kilovolt filter electric field strength. Finally, when the dust signal exceeds the threshold, the guide surface is simultaneously deflected by 15 degrees, maintaining a 1,200-revolution speed and a 12-kilovolt electric field, thus forming a stable purification scheme.

[0085] This invention achieves precise and coordinated control of fan speed and electric field intensity through a real-time dust signal conversion and dynamic parameter generation mechanism combined with the physical optimization design of turbulence suppression configuration. When the dust load changes abruptly, the combined operation of configuration mechanical adjustment and electronic control parameters creates a technical coupling in three aspects: airflow distribution optimization, particle adsorption efficiency improvement, and energy consumption control, ultimately achieving the core effect of continuous and stable purification efficiency under large dust holding conditions.

[0086] To address the problem of inaccurate dust concentration signal conversion leading to distorted load monitoring, this step generates a load monitoring signal by quantizing the original signal, calculating the peak-to-valley difference, filtering the maximum value and combining it with the dust density coefficient, and linearly scaling. This invention provides a specific embodiment: Step 101 involves collecting real-time dust particle concentration data within a preset air purification area and converting the real-time dust particle concentration data into a dust load monitoring signal, specifically including the following steps:

[0087] Step 111: Obtain real-time dust particle concentration data in a preset air purification area through a particulate matter sensor, and quantify the real-time dust particle concentration data to generate an original dust concentration electrical signal.

[0088] In this step, quantization refers to the process of discretizing the continuous analog current signal output by the sensor into digital values ​​through an analog-to-digital converter; the original dust concentration electrical signal refers to the digital voltage signal generated after quantization, whose voltage amplitude is linearly related to the number of dust particles.

[0089] In this embodiment of the invention, firstly, a particulate matter sensor detects the number of dust particles per unit volume within the air purification area. Secondly, the detected analog electrical signal is quantized using an analog-to-digital converter. Finally, a raw dust concentration electrical signal proportional to the particle concentration is generated. This signal represents the real-time dust concentration level through its voltage amplitude.

[0090] Step 112: Divide the original dust concentration electrical signal into multiple continuous time window electrical signal segments, calculate the peak-to-valley difference of the signal amplitude in each electrical signal segment, and generate a set of local fluctuation feature values.

[0091] In this step, segmentation refers to a digital signal processing technique that cuts a continuous electrical signal into segments of equal length according to a preset time length; the peak-to-valley difference of signal amplitude refers to the absolute value of the difference between the voltage values ​​at the highest and lowest points of the signal within a single time window; that is (for example, the peak-to-valley difference of signal amplitude is the absolute value of the peak-to-valley difference of signal amplitude = max(voltage value at the highest point of the signal) – min(voltage value at the lowest point of the electrical signal) within a fixed time window); the set of local fluctuation characteristic values ​​refers to a digital sequence dataset composed of the peak-to-valley differences of all time windows.

[0092] In this embodiment of the invention, the original dust concentration electrical signal is first divided into continuous time window segments of fixed duration. Then, the highest and lowest voltage values ​​are identified within each signal segment. Subsequently, the absolute difference between the two values ​​is calculated. Finally, a set of local fluctuation feature values ​​containing the peak-valley differences of all time windows is generated. This set reflects the instantaneous fluctuation intensity of the dust concentration.

[0093] Step 113: Select the largest local fluctuation feature value from the set of local fluctuation feature values, and multiply the largest local fluctuation feature value with the preset dust density coefficient to generate the dust load index.

[0094] In this step, the preset dust density coefficient refers to a physical parameter constant set according to the specific gravity characteristics of particulate matter and used to correct the intensity of fluctuations; the dust load index refers to the cumulative dust intensity index obtained by multiplying the maximum local fluctuation characteristic value by the dust density coefficient.

[0095] In this embodiment of the invention, all values ​​in the local fluctuation feature set are first traversed; then, the largest feature value is compared and selected; subsequently, this maximum value is multiplied by a preset dust density coefficient; finally, a dust load index characterizing the dust accumulation rate is generated. This dust load index integrates the maximum fluctuation intensity and particulate density characteristics.

[0096] Step 114: Perform linear scaling on the dust load index to generate a dust load monitoring signal.

[0097] In this step, linear scaling refers to the mathematical operation of linearly transforming the input value to the target range by a fixed scaling factor.

[0098] In this embodiment of the invention, the original numerical range of the dust load index is first read, then linearly mapped to a standard signal range using a scaling algorithm, and finally a dust load monitoring signal adapted to the input requirements of the control system is generated. This signal maintains the trend characteristics of the original data while meeting electrical interface specifications.

[0099] As described in steps 111–114, how is the raw dust data converted into a dust load monitoring signal; local fluctuation values (in For the first The voltage value within a time window). That is, the peak-to-valley difference of the signal amplitude within a fixed time window is calculated as: peak-to-valley difference = max(voltage value at the highest point of the signal) – min(voltage value at the lowest point of the signal), then taking the absolute value. Dust load index. ,in This represents the dust density coefficient. Output monitoring signal. (in (This refers to the linear scaling parameter).

[0100] This invention effectively captures sudden fluctuations in dust concentration through signal segmentation and peak-valley difference detection mechanisms; combined with the physical property correction of dust density coefficient, it significantly improves the sensitivity of load monitoring signals to heavily polluted scenarios; and finally, through standardized scaling processing, it ensures that the output signal and the electrical characteristics of the control system are precisely matched, providing a highly reliable input for subsequent purification and control.

[0101] To reduce airflow channel resistance and improve turbulence suppression, this step involves constructing a guide surface coordinate set, optimizing curvature constraints, segmenting unit spacing, and connecting anchor points at reference spacing to form a stacked structure, ultimately assembling a low-resistance flow channel. This invention provides a specific embodiment: Step 102, constructing a turbulence suppression configuration, and configuring the turbulence suppression configuration within the airflow channel of a pre-set air purification device to form a low-resistance airflow channel structure, specifically including the following steps:

[0102] Step 201: Construct a guide surface configuration based on preset large dust-holding airflow characteristic parameters, and measure the discrete point spatial coordinates of the geometric surface of the guide surface configuration to generate an initial surface coordinate set.

[0103] In this step, the preset large dust-bearing airflow characteristic parameters refer to a predefined dataset based on the aerodynamic characteristics of high dust environments, including airflow velocity thresholds, particulate inertial parameters, and turbulence intensity coefficients, which are used to guide the design of the guide surface configuration; the construction operation refers to the technical process of using parametric design tools to transform the airflow characteristic parameters into a three-dimensional surface model.

[0104] In this embodiment of the invention, firstly, the parametric design of the guide surface configuration is performed based on the preset large dust-holding airflow characteristic parameters, and an initial surface model is generated by three-dimensional modeling software; secondly, the discrete point spatial coordinates of the geometric surface of the guide surface configuration are measured, and finally, an initial surface coordinate set containing all coordinate data is output.

[0105] Step 202: Apply curvature constraints to each initial surface coordinate point in the initial surface coordinate set to adjust the position of the initial surface coordinate points and generate an optimized surface coordinate set.

[0106] In this step, the curvature constraint operation refers to the forced adjustment of the coordinate point positions that exceed the allowable range by calculating the principal curvature values ​​of the surface points to make them meet the requirements of smoothness and continuity; the adjustment operation refers to the mathematical processing of iterative optimization of the spatial position of the coordinate points based on the curvature constraint algorithm.

[0107] In this embodiment of the invention, firstly, the curvature value of each coordinate point in the initial surface coordinate set is calculated; secondly, the curvature value is compared with the preset curvature allowable range, and a curvature constraint algorithm is applied to coordinate points that exceed the range to adjust their spatial position; finally, an optimized surface coordinate set with uniform curvature distribution is generated.

[0108] Step 203: Divide the optimized surface coordinate set according to the preset unit spacing threshold to generate multiple independent flow guiding units.

[0109] In this step, the segmentation operation refers to the geometric partitioning method that discretizes the coordinate set of a continuous surface into independent units based on the unit spacing threshold; the independent guiding unit refers to the basic element of the guiding component with independent geometric features generated by the segmentation operation.

[0110] In this embodiment of the invention, firstly, the point spacing data of the optimized surface coordinate set is read; secondly, the continuous coordinate points are divided into independent unit groups according to the preset unit spacing threshold; finally, a unique identifier is assigned to each unit group to generate multiple mutually independent flow guiding units.

[0111] Step 204: Measure the reference spacing between adjacent independent flow guiding units based on the preset stacking direction, locate the installation anchor point of each independent flow guiding unit according to the reference spacing, and connect each installation anchor point to form a flow guiding stacking structure.

[0112] In this step, the reference spacing refers to the minimum vertical distance between adjacent flow guiding units measured according to the stacking direction, which is used to determine the location of the installation anchor point; the connection operation refers to the physical assembly process of fixing the installation anchor point into an integral structure through welding or riveting; the flow guiding stacked structure refers to a composite turbulence suppression component formed by stacking independent flow guiding units according to a preset spatial relationship.

[0113] In this embodiment of the invention, the shortest vertical distance between adjacent independent flow guiding units is first measured based on a preset stacking direction as a reference spacing; then, the three-dimensional coordinates of the installation anchor point of each flow guiding unit are calculated based on the reference spacing; finally, all installation anchor points are connected by welding to form a layered flow guiding stacked structure.

[0114] Step 205: Position the airflow guiding layer structure into the airflow channel of the preset air purification device to form a low-resistance airflow channel structure.

[0115] In this step, the positioning operation refers to the process of precisely installing the airflow channel stack structure to a specified spatial location based on the airflow channel size.

[0116] In this embodiment of the invention, the dimensional parameters of the airflow channel of the preset air purification device are first obtained; then the flow guide stack structure is positioned at a preset angle to the central axis of the channel; finally, the assembly is completed by fixing with bolts to form a low-resistance airflow channel structure that can suppress turbulence.

[0117] This invention, through parametric design of the guide surface configuration, forms a layered structure through curvature optimization and unit segmentation, and finally constructs a low-resistance flow channel in the airflow channel of the purification device. This significantly improves the turbulence suppression capability under high dust load, significantly improves the uniformity of airflow distribution, and significantly reduces system wind resistance energy consumption, thus solving the industry pain point of the sudden drop in efficiency of traditional purifiers when dust changes.

[0118] To improve the response accuracy of dynamic control parameters to changes in dust load trends, this step involves dividing the signal sequence, extracting fluctuation amplitude values, calculating the absolute value of the difference between adjacent fluctuations, inputting the mapping model to generate rotational speed and electric field parameters, and combining them into a parameter set. This invention provides a specific embodiment, step 103, which analyzes the changing trend of the dust load monitoring signal based on a preset adaptive feedback algorithm to generate a dynamic control parameter set, specifically including the following steps:

[0119] Step 301: Receive the dust load monitoring signal, divide the dust load monitoring signal into multiple time period segments based on a preset fixed duration, and generate a continuous sequence of multiple time period signals.

[0120] In this step, the segmentation operation refers to the digital processing of dividing a continuous signal into segments of equal length according to a preset time unit; the time segment signal sequence refers to the data unit generated after the segmentation operation that contains the complete signal waveform within a fixed duration.

[0121] In this embodiment of the invention, a dust load monitoring signal is first received; then, the signal is divided into continuous segments of equal length based on a preset fixed duration; finally, multiple time-segment signal sequences are generated in chronological order. Each sequence contains a complete signal waveform within a fixed duration.

[0122] Step 302: Extract the maximum and minimum signal values ​​from each time period signal sequence, and calculate the difference between the maximum and minimum signal values ​​to generate the fluctuation amplitude value of the time period signal sequence.

[0123] In this step, the fluctuation amplitude value refers to the voltage difference between the maximum and minimum values ​​of the signal in a single time period signal sequence.

[0124] In this embodiment of the invention, firstly, for each time period signal sequence, the highest voltage value and the lowest voltage value in the sequence are identified; secondly, the difference between the highest voltage value and the lowest voltage value is calculated; and finally, a fluctuation amplitude value representing the signal fluctuation intensity within that time period is generated.

[0125] Step 303: Calculate the difference between two adjacent fluctuation amplitude values, obtain the absolute value of the difference, and use the absolute value as the fluctuation change value over the time period.

[0126] In this step, the time period fluctuation change value refers to the absolute value of the difference between two adjacent fluctuation amplitude values.

[0127] In this embodiment of the invention, firstly, two adjacent fluctuation amplitude values ​​are selected in chronological order; secondly, the difference between the subsequent fluctuation amplitude value and the previous fluctuation amplitude value is calculated; then, the absolute value of the difference is taken; and finally, a time period fluctuation change value reflecting the degree of change in the fluctuation intensity of adjacent time periods is generated.

[0128] Step 304: Input the fluctuation values ​​of multiple consecutive time periods into the preset relational mapping model to generate the fan speed parameter value and filter electric field strength parameter value corresponding to the pre-stored relational mapping model.

[0129] In this step, the input operation refers to the electrical connection process of transmitting data to the target system according to a specific interface protocol; the preset relational mapping model refers to an empirical database that pre-stores the correspondence between fluctuation values ​​and control parameters; the fan speed parameter value refers to the control quantity output by the relational mapping model, which represents the number of times the fan rotates per minute; and the filter electric field strength parameter value refers to the control quantity output by the relational mapping model, which represents the kilovolt voltage value between the electrodes.

[0130] In this embodiment of the invention, the fluctuating change values ​​of multiple consecutive time periods are first arranged in chronological order, then input into a pre-stored relational mapping model, and then the optimal control parameters are matched by the corresponding rules built into the model. Finally, the fan speed parameter value and the filter electric field strength parameter value output by the model are generated.

[0131] Step 305: Combine the fan speed parameter value and the filter electric field strength parameter value to generate a dynamic control parameter set.

[0132] In this step, the combination operation refers to the software processing that encapsulates the two parameters into a data packet according to the control protocol format.

[0133] In this embodiment of the invention, the fan speed parameter value and the filter electric field strength parameter value are first read, then the two parameters are merged into a data packet according to the control protocol format, and finally a dynamic control parameter set containing speed and electric field strength control commands is generated.

[0134] This invention accurately captures the dynamic evolution trend of dust load by analyzing the temporal changes in signal fluctuation intensity; it transforms fluctuation characteristics into control parameters based on a pre-stored empirical model to achieve feedforward adjustment of the purification system; and the final generated parameter set provides the equipment with real-time response instructions, significantly improving the control timeliness under large dust conditions.

[0135] It should be noted that steps 301-305 describe how to calculate the difference based on the fluctuations within a time period (i.e., calculate the fluctuation change value between adjacent time periods) and input the data into the mapping model to generate control parameters.

[0136] In a specific example, the process of calculating the fluctuation change value between adjacent time periods is as follows: Let the first... The fluctuation range over the time period is Then the fluctuation change value The relational mapping model is a function: FanSpeed ElectricField ;in and This can be obtained by fitting experimental data, and will not be elaborated further. FanSpeed Output the corresponding fan speed parameter value; ElectricField Used to calculate and output the corresponding filter electric field strength parameter value;

[0137] To ensure precise matching between the fan speed and electric field strength adjustment values ​​and the control parameters, this step extracts parameter values ​​to generate command values, adjusts the motor voltage through the drive module, adjusts the electrode power through the generation module, and reads the actual values ​​as the final output. This invention provides a specific embodiment: Step 104, the dynamic control parameter set is input into the purification control unit in the low-resistance airflow channel structure. Based on the purification control unit, the fan speed and filter electric field strength in the low-resistance airflow channel structure are controlled and adjusted to generate the adjusted fan speed and the adjusted filter electric field strength. This specifically includes the following steps:

[0138] Step 401: Extract the fan speed parameter value and the filter electric field strength parameter value from the set of dynamic control parameters.

[0139] In this step, the extraction operation refers to the technical process of separating specific parameters from a structured dataset, including data field identification and target value capture.

[0140] In this embodiment of the invention, the data structure of the dynamic control parameter set is first parsed, then the fan speed parameter value and the filter electric field strength parameter value are located and separated, and finally these two parameter values ​​are output to the control command generation module.

[0141] Step 402: Match the fan speed parameter value to a preset fan control command range to generate a fan speed control command value, and match the filter electric field strength parameter value to a preset electric field control command range to generate a filter electric field strength control command value.

[0142] In this step, the matching operation refers to a calculation method that maps input parameters to a preset command range, and determines the corresponding command value through boundary comparison; the preset fan control command range refers to a conversion range table of speed parameters and voltage commands predefined according to motor characteristics; the fan speed control command value refers to the quantized value of the motor drive control signal generated by the matching operation; the preset electric field control command range refers to a conversion range table of intensity parameters and power commands predefined according to electrode characteristics; and the filtered electric field intensity control command value refers to the quantized value of the electric field regulation signal generated by the matching operation.

[0143] In this embodiment of the invention, the fan speed parameter value is first matched with a preset fan control command range by interval mapping to generate a corresponding fan speed control command value; at the same time, the filter electric field strength parameter value is matched with a preset electric field control command range by interval mapping to generate a corresponding filter electric field strength control command value.

[0144] Step 403: The fan drive module included in the purification control unit controls and adjusts the preset input voltage value of the fan motor corresponding to the fan speed control command value to generate the controlled and adjusted input voltage value.

[0145] In this step, the fan drive module refers to the power drive circuit component in the purification control unit that converts electrical signals into mechanical motion; the control adjustment operation refers to the physical process of changing the equipment's operating parameters according to control commands; and the input voltage value after control adjustment refers to the measured voltage value actually applied to the motor after adjustment by the fan drive module.

[0146] In this embodiment of the invention, the fan speed control command value is first received through the fan drive module built into the purification control unit, then the input voltage level of the preset fan motor corresponding to the command value is adjusted, and finally the adjusted actual input voltage value is generated.

[0147] Step 404: The discharge power of the preset high-voltage electrode corresponding to the filter electric field strength control command value is controlled and adjusted by the electric field generation module included in the purification control unit to generate the controlled and adjusted discharge power value.

[0148] In this step, the electric field generating module refers to the power amplification component in the purification control unit that converts electrical signals into a high-voltage electric field; the preset discharge power of the high-voltage electrode refers to the baseline discharge power value set according to the electrode specifications; and the controlled and adjusted discharge power value refers to the measured power value actually applied to the electrode after adjustment by the electric field generating module.

[0149] In this embodiment of the invention, the filter electric field strength control command value is first received through the electric field generation module built into the purification control unit, then the discharge power level of the preset high voltage electrode corresponding to the command value is adjusted, and finally the adjusted actual discharge power value is generated.

[0150] Step 405: Read the preset actual speed value of the fan motor under the control and adjustment of the input voltage value, and use the actual speed value as the fan speed after control and adjustment.

[0151] In this step, the actual rotational speed value refers to the quantified data of the rotor rotational speed measured in real time by the motor encoder.

[0152] In this embodiment of the invention, the rotor speed data of the preset fan motor under the current input voltage value is first collected in real time, then the speed data is output as the actual speed value, and finally the value is confirmed as the controlled and adjusted fan speed.

[0153] Step 406: Read the actual filtering electric field strength value of the preset high-voltage electrode under the action of the controlled and adjusted discharge power value, and use the actual filtering electric field strength value as the controlled and adjusted filtering electric field strength.

[0154] In this step, the actual filtered electric field strength value refers to the quantified data of the field strength between the plates measured in real time by the electric field sensor.

[0155] In this embodiment of the invention, the electric field strength data of the preset high-voltage electrode under the current discharge power value is first collected in real time, and then the intensity data is output as the actual filter electric field strength value. Finally, the value is confirmed as the filter electric field strength after control and adjustment.

[0156] This invention generates equipment control commands through parameter matching and conversion, achieves precise adjustment of voltage and power through the drive module, and finally forms closed-loop control through real-time data feedback, which significantly reduces the control error rate of fan speed and electric field strength, and solves the problem of purification efficiency fluctuation caused by equipment response lag in high dust environments.

[0157] To address the issue of uncontrolled fluctuations in purification efficiency when dust load changes abruptly, this step calculates the turbulence intensity value, rotational speed, and electric field influence factors when the threshold is exceeded. It then weights and fuses these factors to generate a collaborative parameter set, matches them to a rule base to generate an instruction set, and executes a stabilization scheme. A specific embodiment of this invention is provided in step 105, where, when the dust load monitoring signal exceeds a preset fluctuation threshold, the turbulence suppression configuration, the controlled and adjusted fan speed, and the controlled and adjusted filter electric field intensity are combined to generate a purification efficiency stabilization scheme for large-capacity dust air purification data analysis and processing. This specifically includes the following steps:

[0158] Step 501: Detect whether the dust load monitoring signal exceeds a preset fluctuation threshold. When it exceeds the preset fluctuation threshold, calculate the comprehensive evaluation value of the guide surface characteristic parameters of the turbulence suppression configuration and generate a turbulence suppression intensity value.

[0159] In this step, the calculation operation refers to the technical action of performing mathematical operations on the input parameters, including addition, subtraction, multiplication, division, and mapping transformation; the guide surface characteristic parameters refer to the set of physical quantities describing the geometric characteristics of the turbulence suppression configuration surface, including the radius of curvature distribution value and the airflow deflection angle value; the comprehensive evaluation value refers to the quantitative result of integrating the guide surface characteristic parameters (i.e., the radius of curvature distribution value and the airflow deflection angle value) through a weighted average algorithm, reflecting the overall performance of the turbulence suppression configuration; the turbulence suppression intensity value refers to the suppression capability level converted based on the comprehensive evaluation value, and the larger the value, the stronger the turbulence control capability.

[0160] In this embodiment of the invention, firstly, it is detected whether the dust load monitoring signal exceeds a preset fluctuation threshold. When it exceeds the threshold, the characteristic parameters of the guiding surface of the turbulence suppression configuration are extracted, including the radius of curvature distribution value and the airflow deflection angle value. Secondly, the weighted average of each characteristic parameter is calculated to generate a comprehensive evaluation value that reflects the turbulence suppression capability. Finally, the evaluation value is mapped to the turbulence suppression intensity value.

[0161] Step 502: Calculate the ratio of the controlled and adjusted fan speed to the preset reference speed as the fan speed influence factor, and calculate the ratio of the controlled and adjusted filter electric field strength to the preset reference electric field strength as the electric field strength influence factor.

[0162] In this step, the preset reference speed refers to the fan speed reference value calibrated under standard operating conditions, which is used to measure the speed adjustment range; the fan speed influence factor refers to the ratio of the adjusted speed to the reference speed, which is used to quantify the contribution of speed change to purification efficiency; the preset reference electric field strength refers to the electric field strength reference value calibrated under standard dust load; the electric field strength influence factor refers to the ratio of the adjusted electric field strength to the reference strength, which reflects the degree of efficiency improvement of electric field adjustment.

[0163] In this embodiment of the invention, the fan speed after control adjustment and the preset reference speed are read first, and the two are divided to generate the fan speed influence factor; the filter electric field strength after control adjustment and the preset reference electric field strength are read second, and the two are divided to generate the electric field strength influence factor.

[0164] Step 503: The turbulence suppression intensity value, the fan speed influence factor, and the electric field intensity influence factor are weighted and fused to generate a collaborative control parameter set.

[0165] In this step, the weighted fusion operation refers to the calculation process of linear superposition after assigning weight coefficients to different parameters; the collaborative control parameter group refers to the set of composite control variables including turbulence suppression intensity, rotational speed influence factor, and electric field influence factor.

[0166] In this embodiment of the invention, firstly, preset weighting coefficients are assigned to the turbulence suppression intensity value, the fan speed influence factor, and the electric field intensity influence factor; secondly, each parameter is multiplied by its weighting coefficient and then summed; finally, a collaborative control parameter group containing the contribution of the three parameters is generated.

[0167] Step 504: Match the collaborative control parameter group to the preset purification rule base to generate a set of equipment control parameter adjustment instructions.

[0168] In this step, the matching operation refers to the technical action of comparing the similarity of the control parameter group with the rule base entries; the preset purification rule base refers to the database that stores the mapping relationship between parameter ranges and equipment control instructions; the equipment control parameter adjustment instruction set refers to the set of executable instructions that includes the fan speed adjustment amount and the electric field strength adjustment amount.

[0169] In this embodiment of the invention, the collaborative control parameter group is first compared with the parameter range in the preset purification rule library; then, the corresponding equipment control rule is selected according to the matching result; finally, a set of equipment control parameter adjustment instructions containing fan speed adjustment and electric field strength adjustment is generated.

[0170] Step 505: Execute the set of equipment control parameter adjustment instructions to generate a stable purification efficiency scheme for large dust volume air purification data analysis and processing.

[0171] In this step, the operation refers to the technical process of converting control commands into device drive signals.

[0172] In this embodiment of the invention, firstly, the various instructions in the device control parameter adjustment instruction set are parsed; secondly, the purification device is driven to perform fan speed adjustment and electric field strength adjustment actions; finally, a technical solution to maintain stable purification efficiency is generated.

[0173] This invention generates intensity values ​​by quantifying the performance characteristics of turbulence suppression configurations, and combines them with the dynamic contributions of fan and electric field adjustment factors to form a collaborative control parameter group through weighted fusion. Based on the rule base, it accurately matches equipment control commands, realizes cross-domain dynamic adaptation of physical turbulence suppression capabilities and electrical control parameters, and overcomes the technical bottleneck of single control dimension failure in high dust sudden change scenarios.

[0174] To achieve the unification of physical dimensions and synergistic effect of multiple control factors, this step involves assigning weight values ​​to generate weighting factors, multiplying them to obtain three types of weighting factors, adding them to generate a superposition value, and splitting continuous values ​​to form a synergistic control parameter set. This invention provides a specific embodiment, step 503, which involves weighted fusion of the turbulence suppression intensity value, the fan speed influence factor, and the electric field intensity influence factor to generate a synergistic control parameter set, specifically including the following steps:

[0175] Step 531: Assign the preset first weight coefficient, the preset second weight coefficient, and the preset third weight coefficient to the turbulence suppression intensity value, the fan speed influence factor, and the electric field intensity influence factor, respectively, to generate turbulence suppression weight allocation value, fan speed weight allocation value, and electric field intensity weight allocation value.

[0176] In this step, the preset first weighting coefficient refers to a predefined quantized coefficient based on the contribution of turbulence suppression, reflecting the priority of aerodynamic structure in collaborative control; the preset second weighting coefficient refers to a predefined quantized coefficient based on the influence of fan speed, reflecting the weight of mechanical control in collaborative control; the preset third weighting coefficient refers to a predefined quantized coefficient based on the influence of electric field strength, reflecting the proportion of electric field control in collaborative control; the allocation operation refers to the calculation process of establishing a binding relationship between the weighting coefficients and the corresponding parameters; the turbulence suppression weight allocation value refers to the number of weighted systems assigned to the turbulence suppression intensity value after the allocation operation; the fan speed weight allocation value refers to the number of weighted systems assigned to the fan speed influence factor after the allocation operation; and the electric field strength weight allocation value refers to the number of weighted systems assigned to the electric field strength influence factor after the allocation operation.

[0177] In this embodiment of the invention, a preset first weighting coefficient, a second weighting coefficient, and a third weighting coefficient are first obtained. Then, the first weighting coefficient is assigned to the turbulence suppression intensity value to generate a turbulence suppression weighting allocation value. At the same time, the second weighting coefficient is assigned to the fan speed influence factor to generate a fan speed weighting allocation value, and the third weighting coefficient is assigned to the electric field intensity influence factor to generate an electric field intensity weighting allocation value.

[0178] Step 532: Multiply the turbulence suppression intensity value and the turbulence suppression weight allocation value to generate a turbulence suppression weighting factor.

[0179] In this step, the turbulence suppression weighting factor refers to the evaluation value obtained by multiplying the turbulence suppression intensity value by the weight allocation value.

[0180] In this embodiment of the invention, the turbulence suppression intensity value and the turbulence suppression weight allocation value are first called, then the multiplication operation between the two is performed, and finally the product result is output as the turbulence suppression weighting factor.

[0181] Step 533: Multiply the fan speed influence factor with the fan speed weight allocation value to generate the fan speed weighting factor.

[0182] In this step, the fan speed weighting factor refers to the evaluation value obtained by multiplying the fan speed influence factor by the weight allocation value.

[0183] In this embodiment of the invention, the fan speed influence factor and the fan speed weight allocation value are first called, then the multiplication operation between the two is performed, and finally the product result is output as the fan speed weighting factor.

[0184] Step 534: Multiply the electric field strength influence factor with the electric field strength weight allocation value to generate the electric field strength weighting factor.

[0185] In this step, the electric field strength weighting factor refers to the evaluation value obtained by multiplying the electric field strength influence factor by the weight allocation value. In this embodiment of the invention, the electric field strength influence factor and the electric field strength weight allocation value are first called, then the multiplication operation between the two is performed, and finally the product result is output as the electric field strength weighting factor.

[0186] Step 535: Perform vector normalization on the turbulence suppression weighting factor, the fan speed weighting factor, and the electric field strength weighting factor to generate a standardized cooperative control vector.

[0187] Step 535 performs the transformation from "absolute quantity" to "relative proportion". For example, before normalization, T_w, F_w, and E_w are the original weighting factors. Their absolute values ​​may differ significantly (e.g., the weight for turbulence suppression is 100, while the weight for fan speed is 10). Using them directly, factors with larger absolute values ​​completely dominate the control system, masking the effects of other factors. After normalization, T_n, F_n, and E_n lose their absolute magnitude meaning, but the proportional relationship between them is preserved and highlighted. For example, a vector (0.8, 0.4, 0.2) explicitly tells the system that the system currently prioritizes turbulence suppression (0.8), followed by fan speed (0.4), and finally electric field strength (0.2).

[0188] In this step, normalization refers to a mathematical method of scaling a set of values ​​proportionally so that they fall into a unified specific interval (such as [0,1]); the standardized cooperative control vector refers to an ordered array composed of three normalized weighting factors used to characterize the comprehensive control state of the system. In this embodiment of the invention, firstly, the turbulence suppression weighting factor T_w, the fan speed weighting factor F_w, and the electric field strength weighting factor E_w are obtained; secondly, the square root of the sum of squares of these three factors is calculated, i.e., the magnitude M = sqrt(T_w² + F_w² + E_w²); then, each weighting factor is divided by this magnitude, and vector normalization is performed to generate the standardized cooperative control vector (T_n, F_n, E_n) = (T_w / M, F_w / M, E_w / M).

[0189] Step 536: Directly use the standardized cooperative control vector as the cooperative control parameter set.

[0190] In this step, the cooperative control parameter set refers to the standardized cooperative control vector (T_n, F_n, E_n). In this embodiment of the invention, the standardized cooperative control vector (T_n, F_n, E_n) generated in step 535 is directly packaged to generate the cooperative control parameter set. The three parameters in this parameter set represent the relative weights and directions of the three dimensions of turbulence suppression, fan speed regulation, and electric field intensity regulation under comprehensive control considerations.

[0191] In this context, relative weights refer to the relative proportions of the three parameters in the vector; direction is a more mathematically abstract concept. In the three-dimensional control space, the X-axis represents turbulence suppression, the Y-axis represents fan speed, and the Z-axis represents electric field strength. Any control state can be represented by a point in this space. The vector acts as a compass; the standardized cooperative control vector is a "command stick" pointing from the origin (0,0,0) to the target state point (T_n, F_n, E_n). The aforementioned direction precisely defines the proportional relationship between the three control dimensions under the current pollution state. It points to the direction of the "cooperative optimal" solution, rather than simply pursuing the maximum value of a single dimension.

[0192] Therefore, the "direction" here reflects the proportional relationship between the components in the integrated control parameter set. The normalized vector is a unit vector, and its direction represents the "inclination" of the control decision in the multi-dimensional (here, three-dimensional) control space. For example, (0.2, 0.7, 0.1), this vector in three-dimensional space is more biased towards the turbulence suppression dimension, indicating that under the current control state, the system regulation is mainly focused on turbulence suppression, while fan speed and electric field strength are relatively auxiliary. This vector (coordinated control parameter set) is then sent to the "purification rule base" for matching. The rule base is like an experienced engineer; upon seeing this vector, it outputs the strategy that needs to be "rapidly diluted" (vector (0.2, 0.7, 0.1)). Then it finds a set of pre-set, mutually cooperating specific equipment control instructions (e.g., increasing fan speed to 1500 rpm, maintaining standard electric field strength, and keeping the default angle of turbulence suppression configuration) and issues them to the equipment for execution.

[0193] The embodiments of the present invention generate a collaborative control parameter set through dynamic weight allocation and weighted fusion, thereby achieving a three-dimensional optimal match between aerodynamic structure, mechanical control and electric field control. This significantly improves the stability of purification efficiency under high dust fluctuation conditions and overcomes the shortcomings of traditional single-dimensional control strategies in response processing.

[0194] Example 2

[0195] Figure 2 This invention provides a schematic diagram of a large-capacity air purification data analysis and processing system, as shown in the embodiment of the invention. Figure 2 As shown, the system includes:

[0196] The conversion module 21 is used to collect real-time dust particle concentration data in a preset air purification area and convert the real-time dust particle concentration data into a dust load monitoring signal.

[0197] Configuration module 22 is used to construct a turbulence suppression configuration and configure the turbulence suppression configuration in the airflow channel of a preset air purification device to form a low-resistance airflow channel structure;

[0198] Analysis module 23 is used to analyze the changing trend of the dust load monitoring signal based on a preset adaptive feedback algorithm and generate a dynamic control parameter set;

[0199] The adjustment module 24 is used to input the dynamic control parameter set into the purification control unit in the low-resistance airflow channel structure, and control and adjust the fan speed and filter electric field intensity in the low-resistance airflow channel structure based on the purification control unit, and generate the controlled and adjusted fan speed and filter electric field intensity.

[0200] The combined module 25 is used to combine the turbulence suppression configuration, the controlled and adjusted fan speed, and the controlled and adjusted filter electric field strength when the dust load monitoring signal exceeds the preset fluctuation threshold to generate a stable purification efficiency scheme for large dust volume air purification data analysis and processing.

[0201] Figure 2 The aforementioned large-capacity air purification data analysis and processing system can perform... Figure 1 The implementation principle and technical effects of the large-capacity dust air purification data analysis and processing method described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the large-capacity dust air purification data analysis and processing system described in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0202] In one possible design, Figure 2 The large-capacity air purification data analysis and processing system of the embodiment shown can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0203] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0204] The processing component 32 is used for: collecting real-time dust particle concentration data within a preset air purification area and converting the real-time dust particle concentration data into a dust load monitoring signal; constructing a turbulence suppression configuration and configuring the turbulence suppression configuration within the airflow channel of a preset air purification device to form a low-resistance airflow channel structure; analyzing the changing trend of the dust load monitoring signal based on a preset adaptive feedback algorithm to generate a dynamic control parameter set; inputting the dynamic control parameter set into the purification control unit in the low-resistance airflow channel structure, and controlling and adjusting the fan speed and filter electric field intensity in the low-resistance airflow channel structure based on the purification control unit to generate the controlled and adjusted fan speed and filter electric field intensity; when the dust load monitoring signal exceeds a preset fluctuation threshold, combining the turbulence suppression configuration, the controlled and adjusted fan speed, and the controlled and adjusted filter electric field intensity to generate a purification efficiency stabilization scheme for large-capacity air purification data analysis and processing.

[0205] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0206] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0207] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0208] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0209] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0210] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0211] This invention also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown illustrates a method for analyzing and processing large-capacity dust-laden air purification data.

[0212] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0213] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A large dust capacity air cleaning data analysis processing method, characterized by, include: The process involves collecting real-time dust particle concentration data within a preset air purification area and converting this data into a dust load monitoring signal. This includes: acquiring real-time dust particle concentration data within the preset air purification area using a particulate sensor; quantizing the real-time dust particle concentration data to generate a raw dust concentration electrical signal; dividing the raw dust concentration electrical signal into multiple electrical signal segments with consecutive time windows; calculating the peak-to-valley difference in signal amplitude within each electrical signal segment to generate a set of local fluctuation characteristic values; selecting the largest local fluctuation characteristic value from the set of local fluctuation characteristic values; multiplying the largest local fluctuation characteristic value by a preset dust density coefficient to generate a dust load index; and linearly scaling the dust load index to generate a dust load monitoring signal. Constructing a turbulence suppression configuration and placing it within the airflow channel of a preset air purification device to form a low-resistance airflow channel structure includes: constructing a guide surface configuration based on preset large dust-holding airflow characteristic parameters; measuring the discrete point spatial coordinates of the geometric surface of the guide surface configuration to generate an initial surface coordinate set; applying curvature constraints to each initial surface coordinate point in the initial surface coordinate set to adjust the position of the initial surface coordinate points, generating an optimized surface coordinate set; dividing the optimized surface coordinate set according to a preset unit spacing threshold to generate multiple independent guide units; measuring the reference spacing between adjacent independent guide units based on a preset stacking direction; locating the installation anchor point of each independent guide unit according to the reference spacing; connecting each installation anchor point to form a guide stack structure; and positioning the guide stack structure within the preset airflow channel of the air purification device to form a low-resistance airflow channel structure. The changing trend of the dust load monitoring signal is analyzed based on a preset adaptive feedback algorithm to generate a dynamic control parameter set. The set of dynamic control parameters is input into the purification control unit in the low-resistance airflow channel structure. Based on the purification control unit, the fan speed and filter electric field intensity in the low-resistance airflow channel structure are controlled and adjusted to generate the controlled and adjusted fan speed and filter electric field intensity. When the dust load monitoring signal exceeds the preset fluctuation threshold, the turbulence suppression configuration, the controlled and adjusted fan speed, and the controlled and adjusted filter electric field strength are combined to generate a purification efficiency stabilization scheme for large dust volume air purification data analysis and processing.

2. The method according to claim 1, characterized in that, The changing trend of the dust load monitoring signal is analyzed based on a preset adaptive feedback algorithm to generate a dynamic control parameter set, including: The dust load monitoring signal is received, and the dust load monitoring signal is divided into multiple consecutive time period signals based on a preset fixed duration. Extract the maximum and minimum signal values ​​from each time period signal sequence, and calculate the difference between the maximum and minimum signal values ​​to generate the fluctuation amplitude value of the time period signal sequence; Calculate the difference between two adjacent fluctuation amplitude values, obtain the absolute value of the difference, and use the absolute value as the fluctuation change value over a time period. The fluctuation values ​​of multiple consecutive time periods are input into a preset relational mapping model to generate the fan speed parameter value and filter electric field strength parameter value corresponding to the preset relational mapping model; The fan speed parameter value and the filter electric field strength parameter value are combined to generate a dynamic control parameter set.

3. The method according to claim 1, characterized in that, The dynamic control parameter set is input into the purification control unit in the low-resistance airflow channel structure. Based on the purification control unit, the fan speed and filter electric field intensity in the low-resistance airflow channel structure are controlled and adjusted to generate the adjusted fan speed and filter electric field intensity, including: Extract fan speed parameter values ​​and filter electric field strength parameter values ​​from the dynamic control parameter set; The fan speed parameter value is matched to a preset fan control command range to generate a fan speed control command value, and the filter electric field strength parameter value is matched to a preset electric field control command range to generate a filter electric field strength control command value. The fan drive module included in the purification control unit controls and adjusts the preset input voltage value of the fan motor corresponding to the fan speed control command value, and generates the controlled and adjusted input voltage value. The discharge power of the preset high-voltage electrode corresponding to the filter electric field strength control command value is controlled and adjusted by the electric field generation module included in the purification control unit, thereby generating the controlled and adjusted discharge power value. Read the actual speed value of the preset fan motor under the control and adjustment of the input voltage value, and use the actual speed value as the fan speed after control and adjustment; The actual filtering electric field strength value of the preset high-voltage electrode under the action of the controlled and adjusted discharge power value is read, and the actual filtering electric field strength value is used as the controlled and adjusted filtering electric field strength.

4. The method according to claim 1, characterized in that, When the dust load monitoring signal exceeds a preset fluctuation threshold, the turbulence suppression configuration, the controlled and adjusted fan speed, and the controlled and adjusted filter electric field strength are combined to generate a stable purification efficiency scheme for large-capacity air purification data analysis and processing, including: The dust load monitoring signal is detected to see if it exceeds a preset fluctuation threshold. When it exceeds the preset fluctuation threshold, the comprehensive evaluation value of the guide surface characteristic parameters of the turbulence suppression configuration is calculated to generate a turbulence suppression intensity value. The ratio of the controlled and adjusted fan speed to the preset reference speed is calculated as the fan speed influence factor, and the ratio of the controlled and adjusted filter electric field strength to the preset reference electric field strength is calculated as the electric field strength influence factor. The turbulence suppression intensity value, the fan speed influence factor, and the electric field intensity influence factor are weighted and fused to generate a collaborative control parameter set; The collaborative control parameter group is matched to a preset purification rule base to generate a set of equipment control parameter adjustment instructions; The device control parameter adjustment instruction set is executed to generate a stable purification efficiency scheme for large-capacity dust air purification data analysis and processing.

5. The method according to claim 4, characterized in that, The turbulence suppression intensity value, the fan speed influence factor, and the electric field intensity influence factor are weighted and fused to generate a collaborative control parameter set, including: The preset first weighting coefficient, the preset second weighting coefficient, and the preset third weighting coefficient are respectively assigned to the turbulence suppression intensity value, the fan speed influence factor, and the electric field intensity influence factor to generate turbulence suppression weight allocation value, fan speed weight allocation value, and electric field intensity weight allocation value; The turbulence suppression intensity value is multiplied by the turbulence suppression weight allocation value to generate a turbulence suppression weighting factor. The fan speed influence factor is multiplied by the fan speed weight allocation value to generate the fan speed weighting factor. The electric field strength influence factor is multiplied by the electric field strength weight allocation value to generate the electric field strength weighting factor. The turbulence suppression weighting factor, the fan speed weighting factor, and the electric field strength weighting factor are vector normalized to generate a standardized cooperative control vector. The standardized cooperative control vector is directly used as the cooperative control parameter set.

6. A large-capacity dust-absorbing air purification data analysis and processing system, applied to the large-capacity dust-absorbing air purification data analysis and processing method according to any one of claims 1-5, characterized in that, include: The conversion module is used to collect real-time dust particle concentration data in a preset air purification area and convert the real-time dust particle concentration data into a dust load monitoring signal. A configuration module is used to construct a turbulence suppression configuration, which is then configured within the airflow channel of a preset air purification device to form a low-resistance airflow channel structure. The analysis module is used to analyze the changing trend of the dust load monitoring signal based on a preset adaptive feedback algorithm and generate a dynamic control parameter set. The adjustment module is used to input the dynamic control parameter set into the purification control unit in the low-resistance airflow channel structure, and control and adjust the fan speed and filter electric field intensity in the low-resistance airflow channel structure based on the purification control unit, and generate the controlled and adjusted fan speed and filter electric field intensity. The combined module is used to combine the turbulence suppression configuration, the controlled and adjusted fan speed, and the controlled and adjusted filter electric field strength when the dust load monitoring signal exceeds the preset fluctuation threshold, to generate a stable purification efficiency scheme for large dust volume air purification data analysis and processing.

7. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the large-capacity air purification data analysis and processing method as described in any one of claims 1 to 5.

8. A computer storage medium, characterized in that, The device contains a computer program, which, when executed by a computer, implements a method for analyzing and processing large-capacity air purification data as described in any one of claims 1 to 5.

Images